Overview

Dataset statistics

Number of variables12
Number of observations3237593
Missing cells4248777
Missing cells (%)10.9%
Duplicate rows23514
Duplicate rows (%)0.7%
Total size in memory296.4 MiB
Average record size in memory96.0 B

Variable types

Categorical3
Numeric8
Unsupported1

Alerts

Dataset has 23514 (0.7%) duplicate rowsDuplicates
SEXO is highly imbalanced (50.3%)Imbalance
SUICIDIO is highly imbalanced (93.6%)Imbalance
ASSISTMED has 1475120 (45.6%) missing valuesMissing
ESC has 539840 (16.7%) missing valuesMissing
ESTCIV has 188999 (5.8%) missing valuesMissing
HORAOBITO has 196892 (6.1%) missing valuesMissing
NATURAL has 957086 (29.6%) missing valuesMissing
OCUP has 760072 (23.5%) missing valuesMissing
RACACOR has 130117 (4.0%) missing valuesMissing
HORAOBITO is an unsupported type, check if it needs cleaning or further analysisUnsupported
ESC has 64843 (2.0%) zerosZeros

Reproduction

Analysis started2024-04-21 16:47:13.784793
Analysis finished2024-04-21 16:48:17.173318
Duration1 minute and 3.39 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

ASSISTMED
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1475120
Missing (%)45.6%
Memory size24.7 MiB
1.0
1387317 
2.0
193587 
9.0
181569 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5287419
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1387317
42.9%
2.0 193587
 
6.0%
9.0 181569
 
5.6%
(Missing) 1475120
45.6%

Length

2024-04-21T13:48:17.670491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T13:48:17.924678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1387317
78.7%
2.0 193587
 
11.0%
9.0 181569
 
10.3%

Most occurring characters

ValueCountFrequency (%)
. 1762473
33.3%
0 1762473
33.3%
1 1387317
26.2%
2 193587
 
3.7%
9 181569
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5287419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1762473
33.3%
0 1762473
33.3%
1 1387317
26.2%
2 193587
 
3.7%
9 181569
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5287419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1762473
33.3%
0 1762473
33.3%
1 1387317
26.2%
2 193587
 
3.7%
9 181569
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5287419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1762473
33.3%
0 1762473
33.3%
1 1387317
26.2%
2 193587
 
3.7%
9 181569
 
3.4%

DTOBITO
Real number (ℝ)

Distinct4383
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15758464
Minimum1012006
Maximum31122017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:18.266587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1012006
5-th percentile2072008
Q18082007
median16022016
Q323102007
95-th percentile29122013
Maximum31122017
Range30110011
Interquartile range (IQR)15020000

Descriptive statistics

Standard deviation8789155.4
Coefficient of variation (CV)0.55774189
Kurtosis-1.1896495
Mean15758464
Median Absolute Deviation (MAD)7920000
Skewness0.01314967
Sum5.1019492 × 1013
Variance7.7249252 × 1013
MonotonicityNot monotonic
2024-04-21T13:48:18.532588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19012015 1050
 
< 0.1%
14062016 1045
 
< 0.1%
7022014 1023
 
< 0.1%
9022014 1021
 
< 0.1%
17062016 1020
 
< 0.1%
10022014 1019
 
< 0.1%
13062016 1019
 
< 0.1%
16062016 1006
 
< 0.1%
24062016 997
 
< 0.1%
8022014 997
 
< 0.1%
Other values (4373) 3227396
99.7%
ValueCountFrequency (%)
1012006 662
< 0.1%
1012007 624
< 0.1%
1012008 825
< 0.1%
1012009 695
< 0.1%
1012010 752
< 0.1%
1012011 771
< 0.1%
1012012 687
< 0.1%
1012013 787
< 0.1%
1012014 859
< 0.1%
1012015 840
< 0.1%
ValueCountFrequency (%)
31122017 746
< 0.1%
31122016 768
< 0.1%
31122015 723
< 0.1%
31122014 748
< 0.1%
31122013 730
< 0.1%
31122012 693
< 0.1%
31122011 680
< 0.1%
31122010 724
< 0.1%
31122009 663
< 0.1%
31122008 622
< 0.1%

ESC
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing539840
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean3.6110956
Minimum0
Maximum9
Zeros64843
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:18.807949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5378352
Coefficient of variation (CV)0.70278815
Kurtosis0.36373429
Mean3.6110956
Median Absolute Deviation (MAD)1
Skewness1.1959698
Sum9741844
Variance6.4406076
MonotonicityNot monotonic
2024-04-21T13:48:19.021378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 722256
22.3%
3 623304
19.3%
9 409313
12.6%
4 390218
12.1%
1 314091
9.7%
5 173728
 
5.4%
0 64843
 
2.0%
(Missing) 539840
16.7%
ValueCountFrequency (%)
0 64843
 
2.0%
1 314091
9.7%
2 722256
22.3%
3 623304
19.3%
4 390218
12.1%
5 173728
 
5.4%
9 409313
12.6%
ValueCountFrequency (%)
9 409313
12.6%
5 173728
 
5.4%
4 390218
12.1%
3 623304
19.3%
2 722256
22.3%
1 314091
9.7%
0 64843
 
2.0%

ESTCIV
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing188999
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean2.4289184
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:19.234090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4293908
Coefficient of variation (CV)0.5884886
Kurtosis9.5578298
Mean2.4289184
Median Absolute Deviation (MAD)1
Skewness2.5790639
Sum7404786
Variance2.043158
MonotonicityNot monotonic
2024-04-21T13:48:19.435713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1170466
36.2%
3 853982
26.4%
1 679997
21.0%
4 229326
 
7.1%
9 82623
 
2.6%
5 32200
 
1.0%
(Missing) 188999
 
5.8%
ValueCountFrequency (%)
1 679997
21.0%
2 1170466
36.2%
3 853982
26.4%
4 229326
 
7.1%
5 32200
 
1.0%
9 82623
 
2.6%
ValueCountFrequency (%)
9 82623
 
2.6%
5 32200
 
1.0%
4 229326
 
7.1%
3 853982
26.4%
2 1170466
36.2%
1 679997
21.0%

HORAOBITO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing196892
Missing (%)6.1%
Memory size24.7 MiB

IDADE
Real number (ℝ)

Distinct250
Distinct (%)< 0.1%
Missing649
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean461.14141
Minimum0
Maximum999
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:19.723066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile421
Q1454
median469
Q3481
95-th percentile492
Maximum999
Range999
Interquartile range (IQR)27

Descriptive statistics

Standard deviation54.780199
Coefficient of variation (CV)0.11879262
Kurtosis41.925204
Mean461.14141
Median Absolute Deviation (MAD)13
Skewness-0.74688913
Sum1.4926889 × 109
Variance3000.8702
MonotonicityNot monotonic
2024-04-21T13:48:20.008963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
481 75080
 
2.3%
480 75014
 
2.3%
479 74653
 
2.3%
478 74377
 
2.3%
482 73590
 
2.3%
477 73515
 
2.3%
483 72136
 
2.2%
476 71786
 
2.2%
484 70603
 
2.2%
475 69502
 
2.1%
Other values (240) 2506688
77.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 367
< 0.1%
2 114
 
< 0.1%
3 73
 
< 0.1%
4 40
 
< 0.1%
5 339
< 0.1%
6 39
 
< 0.1%
7 37
 
< 0.1%
8 38
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
999 8815
0.3%
529 1
 
< 0.1%
527 1
 
< 0.1%
522 1
 
< 0.1%
520 1
 
< 0.1%
519 1
 
< 0.1%
518 2
 
< 0.1%
517 3
 
< 0.1%
516 3
 
< 0.1%
515 7
 
< 0.1%

LOCOCOR
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.5485047
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:20.228152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0428696
Coefficient of variation (CV)0.6734688
Kurtosis4.6988093
Mean1.5485047
Median Absolute Deviation (MAD)0
Skewness2.0291656
Sum5013425
Variance1.0875771
MonotonicityNot monotonic
2024-04-21T13:48:20.447965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2400433
74.1%
3 480252
 
14.8%
2 178466
 
5.5%
4 92684
 
2.9%
5 81809
 
2.5%
9 3947
 
0.1%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
1 2400433
74.1%
2 178466
 
5.5%
3 480252
 
14.8%
4 92684
 
2.9%
5 81809
 
2.5%
9 3947
 
0.1%
ValueCountFrequency (%)
9 3947
 
0.1%
5 81809
 
2.5%
4 92684
 
2.9%
3 480252
 
14.8%
2 178466
 
5.5%
1 2400433
74.1%

NATURAL
Real number (ℝ)

MISSING 

Distinct237
Distinct (%)< 0.1%
Missing957086
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean803.19575
Minimum0
Maximum999
Zeros389
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:20.694446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q1829
median835
Q3835
95-th percentile835
Maximum999
Range999
Interquartile range (IQR)6

Descriptive statistics

Standard deviation141.68118
Coefficient of variation (CV)0.17639682
Kurtosis18.802808
Mean803.19575
Median Absolute Deviation (MAD)0
Skewness-4.5088083
Sum1.8316935 × 109
Variance20073.556
MonotonicityNot monotonic
2024-04-21T13:48:20.976327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
835 1394096
43.1%
831 204051
 
6.3%
829 146988
 
4.5%
826 91955
 
2.8%
800 82237
 
2.5%
841 51469
 
1.6%
827 39458
 
1.2%
823 36739
 
1.1%
190 31927
 
1.0%
825 27027
 
0.8%
Other values (227) 174560
 
5.4%
(Missing) 957086
29.6%
ValueCountFrequency (%)
0 389
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%
4 55
 
< 0.1%
6 7
 
< 0.1%
8 2129
0.1%
9 2
 
< 0.1%
10 335
 
< 0.1%
11 12
 
< 0.1%
12 3
 
< 0.1%
ValueCountFrequency (%)
999 10602
 
0.3%
998 4
 
< 0.1%
853 631
 
< 0.1%
852 3338
 
0.1%
851 1951
 
0.1%
850 5321
 
0.2%
843 6894
 
0.2%
842 5965
 
0.2%
841 51469
 
1.6%
835 1394096
43.1%

OCUP
Real number (ℝ)

MISSING 

Distinct2220
Distinct (%)0.1%
Missing760072
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean826337.31
Minimum0
Maximum999994
Zeros57
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:21.249175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile231205
Q1715210
median999992
Q3999993
95-th percentile999993
Maximum999994
Range999994
Interquartile range (IQR)284783

Descriptive statistics

Standard deviation265871.13
Coefficient of variation (CV)0.32174649
Kurtosis0.49599816
Mean826337.31
Median Absolute Deviation (MAD)1
Skewness-1.3292233
Sum2.0472681 × 1012
Variance7.068746 × 1010
MonotonicityNot monotonic
2024-04-21T13:48:21.547464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999993 828601
25.6%
999992 559932
17.3%
998999 113908
 
3.5%
715210 68630
 
2.1%
141410 53783
 
1.7%
512105 39145
 
1.2%
782305 33212
 
1.0%
621005 27681
 
0.9%
999991 26568
 
0.8%
999994 26185
 
0.8%
Other values (2210) 699876
21.6%
(Missing) 760072
23.5%
ValueCountFrequency (%)
0 57
 
< 0.1%
10105 10
 
< 0.1%
10110 7
 
< 0.1%
10115 2
 
< 0.1%
10205 98
 
< 0.1%
10210 131
 
< 0.1%
10215 47
 
< 0.1%
10305 21
 
< 0.1%
10310 3121
0.1%
10315 34
 
< 0.1%
ValueCountFrequency (%)
999994 26185
 
0.8%
999993 828601
25.6%
999992 559932
17.3%
999991 26568
 
0.8%
998999 113908
 
3.5%
992225 1914
 
0.1%
992220 15
 
< 0.1%
992215 1
 
< 0.1%
992210 129
 
< 0.1%
992205 287
 
< 0.1%

RACACOR
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing130117
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1.5957735
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 MiB
2024-04-21T13:48:21.820500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1350783
Coefficient of variation (CV)0.71130288
Kurtosis0.53122662
Mean1.5957735
Median Absolute Deviation (MAD)0
Skewness1.5380467
Sum4958828
Variance1.2884028
MonotonicityNot monotonic
2024-04-21T13:48:22.022564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2354375
72.7%
4 525380
 
16.2%
2 182874
 
5.6%
3 43535
 
1.3%
5 1307
 
< 0.1%
9 5
 
< 0.1%
(Missing) 130117
 
4.0%
ValueCountFrequency (%)
1 2354375
72.7%
2 182874
 
5.6%
3 43535
 
1.3%
4 525380
 
16.2%
5 1307
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
9 5
 
< 0.1%
5 1307
 
< 0.1%
4 525380
 
16.2%
3 43535
 
1.3%
2 182874
 
5.6%
1 2354375
72.7%

SEXO
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.7 MiB
1
1797069 
2
1439711 
0
 
687
9
 
126

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3237593
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 1797069
55.5%
2 1439711
44.5%
0 687
 
< 0.1%
9 126
 
< 0.1%

Length

2024-04-21T13:48:22.312459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T13:48:22.541426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1797069
55.5%
2 1439711
44.5%
0 687
 
< 0.1%
9 126
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 1797069
55.5%
2 1439711
44.5%
0 687
 
< 0.1%
9 126
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3237593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1797069
55.5%
2 1439711
44.5%
0 687
 
< 0.1%
9 126
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3237593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1797069
55.5%
2 1439711
44.5%
0 687
 
< 0.1%
9 126
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3237593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1797069
55.5%
2 1439711
44.5%
0 687
 
< 0.1%
9 126
 
< 0.1%

SUICIDIO
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.7 MiB
0
3212973 
1
 
24620

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3237593
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3212973
99.2%
1 24620
 
0.8%

Length

2024-04-21T13:48:22.842387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T13:48:23.046821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3212973
99.2%
1 24620
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3212973
99.2%
1 24620
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3237593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3212973
99.2%
1 24620
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3237593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3212973
99.2%
1 24620
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3237593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3212973
99.2%
1 24620
 
0.8%

Interactions

2024-04-21T13:48:05.289123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:43.326293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:47.296619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:50.179764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:53.230375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:56.407671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:59.424993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:02.225124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:05.834099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:43.963021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:47.661474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:50.586854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:53.643833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:56.790425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:59.808284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:02.591220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:06.709206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:44.406197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:47.985942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:50.940724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:54.041443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:57.146122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:00.137871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:02.926770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:07.097586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:44.888458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:48.358752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:51.330361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:54.441861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:57.534227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:00.483170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:03.318962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:07.510168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:45.414916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:48.736186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:51.715835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:54.841979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:57.911236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:00.818461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:03.728631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:07.900693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:45.819488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:49.063383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:52.052377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:55.206285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:58.235973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:01.157342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:04.082622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:08.289577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:46.261887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:49.414783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:52.404100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:55.593404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:58.596011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:01.470580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:04.470576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:08.728469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:46.797327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:49.788504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:52.804929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:56.003481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:47:58.979507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:01.855078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-21T13:48:04.813221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-04-21T13:48:23.290176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ASSISTMEDDTOBITOESCESTCIVIDADELOCOCORNATURALOCUPRACACORSEXOSUICIDIO
ASSISTMED1.000-0.0010.059-0.046-0.1110.4620.010-0.0120.0560.0810.160
DTOBITO-0.0011.000-0.0000.0020.003-0.0020.0010.003-0.0000.0020.000
ESC0.059-0.0001.000-0.009-0.2000.0380.143-0.188-0.0680.0930.057
ESTCIV-0.0460.002-0.0091.0000.373-0.038-0.0030.141-0.1070.2090.072
IDADE-0.1110.003-0.2000.3731.000-0.038-0.1160.318-0.1690.0930.016
LOCOCOR0.462-0.0020.038-0.038-0.0381.0000.009-0.0220.0280.0720.125
NATURAL0.0100.0010.143-0.003-0.1160.0091.000-0.016-0.1410.0300.010
OCUP-0.0120.003-0.1880.1410.318-0.022-0.0161.000-0.0440.2290.048
RACACOR0.056-0.000-0.068-0.107-0.1690.028-0.141-0.0441.0000.0320.013
SEXO0.0810.0020.0930.2090.0930.0720.0300.2290.0321.0000.042
SUICIDIO0.1600.0000.0570.0720.0160.1250.0100.0480.0130.0421.000

Missing values

2024-04-21T13:48:09.092869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T13:48:11.186597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ASSISTMEDDTOBITOESCESTCIVHORAOBITOIDADELOCOCORNATURALOCUPRACACORSEXOSUICIDIO
0NaN90220069.04.0130.0463.03.0NaN999993.01.010
1NaN260120062.02.01130.0481.03.0NaN214305.0NaN10
21.0190320062.03.01520.0493.03.0NaN514105.01.020
31.0211120062.01.01000.0489.03.077.0214305.01.010
4NaN160420069.03.02130.0480.03.0NaNNaN1.020
5NaN70520063.03.02015.0468.03.0NaNNaN1.010
62.0280120069.03.02230.0487.03.0NaN214305.04.020
7NaN13122006NaNNaN2020.0999.09.0835.0NaNNaN10
8NaN280520062.02.01706.0450.05.0835.0512120.01.010
91.06042006NaN3.0820.0487.03.0835.0999992.03.020
ASSISTMEDDTOBITOESCESTCIVHORAOBITOIDADELOCOCORNATURALOCUPRACACORSEXOSUICIDIO
32375831.0211020179.09.0NaN482.01.0999.0999993.0NaN10
3237584NaN41120173.02.01515.0491.02.0NaN999993.01.020
3237585NaN270920172.04.02035.0474.03.0829.0NaN1.010
3237586NaN71020179.0NaN419.0486.03.0835.0999993.0NaN20
3237587NaN280620171.01.0NaN441.05.0835.0999993.0NaN20
3237588NaN13042017NaNNaN1840.0465.03.0835.0NaN1.010
3237589NaN190420172.01.0937.0455.03.0829.0999993.04.010
32375901.0100120172.03.0615.0487.02.0835.0999993.01.010
32375911.0100820172.02.0500.0486.03.0835.0999993.01.010
32375922.06052017NaNNaN1030.0215.05.0835.0NaN1.020

Duplicate rows

Most frequently occurring

ASSISTMEDDTOBITOESCESTCIVIDADELOCOCORNATURALOCUPRACACORSEXOSUICIDIO# duplicates
13231.031220122.03.0485.01.0NaN999992.01.0205
42581.010062016NaNNaN301.01.0835.0NaN1.0105
1561.010520142.03.0489.01.0835.0999993.01.0204
3541.010920122.03.0484.01.0NaN999992.01.0204
6601.020520152.02.0472.01.0835.0999992.01.0204
7221.020720122.03.0485.01.0NaN999992.01.0204
8921.021220072.02.0472.01.0835.0999992.01.0204
9021.021220123.02.0470.01.0NaN999993.01.0104
12861.031120122.03.0486.01.0NaN999992.01.0204
15271.04052017NaNNaN301.01.0835.0NaN1.0104